The present invention relates to a system and a method for control and/or analytics of an industrial process.
The following discussion of related art is provided to assist the reader in understanding the advantages of the invention, and is not to be construed as an admission that this related art is prior art to this invention.
A plurality of plants that undertake process control generally fulfill simple automation and closed-loop control tasks. These tasks are generally performed by automation units that are installed on site and thus in the vicinity of the process to be automated. Often such plants then also consist of a plurality of mostly also spatially-separated, smaller automation units, which then results in the individual process tasks also running in a distributed fashion. Such smaller automation units, because of their restricted processing power, are not likely to be capable of emulating complex closed-loop control structures and/or simulation strategies, as are possible in higher classes of automation device. Such more complex closed-loop control strategies, which can require a significant processing capacity, can for example be so-called model predictive controls (Model Predictive Control, MPC), as are preferably used in process engineering processes. Frequently there is also the desire to set up complex closed-loop controls which are based on comprehensive historical data and to use these for example in so-called Support Vector Machines (SVM), in order to be able to undertake optimizations to the process on this basis. Therefore such processing-intensive process engineering processes or data analytics models are frequently automated in the superordinate control and monitoring system of the plant.
We are currently experiencing a trend in the direction of central data analytics in external processing units (so-called cloud based analytics). Because of its comprehensive analytics methods and the mostly self-learning techniques, cloud-based analytics allows a significant enhancement of the process controls. However the cloud-processing approaches are often not real-time-capable, because the data must be transferred from sensors or actuators of the industrial process from the plant into the external processing unit, in order to analyze it there. Thereafter the analytics result is to be returned for further actions in order for it to become effective for the processes in the plant, which overall means unacceptable time losses. Closed control circuits—especially when, because of the closed-loop control speed, comparatively fast sampling rates become necessary—are problematic with cloud-based methods on account of the insecure but at least less deterministic communication. The major sources of such time losses lie a) in the data acquisition, the pre-processing and the compression, b) in the transmission of data into the cloud and c) in the analytics and result computation itself. To ameliorate the problem of latency times in cloud-based systems, attempts are being made to make data collection possible by suitable faster hardware. Furthermore it is proposed that the data undergoes processing in order then for only a reduced amount of data to be transmitted into the cloud-based system. A further point in the approach relates to the transmission of the data, in that attempts are being made to provide faster transmission channels with corresponding bandwidths. As part of the analytics itself the cloud-based systems are equipped with high-end computers on which efficient algorithms are then to run.
It has been shown that high bandwidths and low wait times alone are often not sufficient. This is especially true when critical industrial processes are involved.
The object of the invention is thus to specify an alternate facility and an alternate method that support cloud-based process control and/or analytics in the industrial environment deterministically in real time.